Roberto Calandra · Ignasi Clavera Gilaberte · Frank Hutter · Joaquin Vanschoren · Jane Wang

Fri Dec 13th 08:00 AM -- 06:00 PM @ West Ballroom B
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Recent years have seen rapid progress in meta­learning methods, which learn (and optimize) the performance of learning methods based on data, generate new learning methods from scratch, and learn to transfer knowledge across tasks and domains. Meta­learning can be seen as the logical conclusion of the arc that machine learning has undergone in the last decade, from learning classifiers, to learning representations, and finally to learning algorithms that themselves acquire representations and classifiers. The ability to improve one’s own learning capabilities through experience can also be viewed as a hallmark of intelligent beings, and there are strong connections with work on human learning in neuroscience. The goal of this workshop is to bring together researchers from all the different communities and topics that fall under the umbrella of meta­learning. We expect that the presence of these different communities will result in a fruitful exchange of ideas and stimulate an open discussion about the current challenges in meta­learning, as well as possible solutions.

09:00 AM Opening Remarks <span> <a href="#"></a> </span>
09:10 AM Meta-learning as hierarchical modeling (Talk) Erin Grant
09:40 AM How Meta-Learning Could Help Us Accomplish Our Grandest AI Ambitions, and Early, Exotic Steps in that Direction (Talk) Jeff Clune
10:10 AM Poster Spotlights 1 (Spotlight)
10:30 AM Coffee/Poster session 1 (Poster Session)
Shiro Takagi, Khurram Javed, Johanna Sommer, Amr Sharaf, Pierluca D'Oro, Ying Wei, Sivan Doveh, Colin White, Santiago Gonzalez, Cuong Nguyen, mao li, Tianhe (Kevin) Yu, Tiago Ramalho, Masahiro Nomura, Ahsan Alvi, Jean-Francois Ton, Ronny Huang, Jessica Lee, Sebastian Flennerhag, Michael Zhang, Abe Friesen, Paul Blomstedt, Alina Dubatovka, Sergey Bartunov, Subin Yi, Iaroslav Shcherbatyi, Christian Simon, Zeyuan Shang, David MacLeod, Lu Liu, Liam Fowl, Diego Parente Paiva Mesquita, Deirdre Quillen
11:30 AM Interaction of Model-based RL and Meta-RL (Talk) Pieter Abbeel
12:00 PM Discussion 1 (Discussion Panel)
02:00 PM Abstraction & Meta-Reinforcement Learning (Talk) Dave Abel
02:30 PM Scalable Meta-Learning (Talk) Raia Hadsell
03:00 PM Poster Spotlights 2 (Spotlight)
03:20 PM Coffee/Poster session 2 (Poster Session)
Xingyou Song, Puneet Mangla, David Salinas, Zhenxun Zhuang, Leo Feng, Shell Xu Hu, Raul Puri, Wesley J Maddox, Aniruddh Raghu, Prudencio Tossou, Mingzhang Yin, Ishita Dasgupta, Kangwook Lee, Ferran Alet, Zhen Xu, Jörg KH Franke, James Harrison, Jonathan Warrell, Guneet S Dhillon, Arber Zela, Xin Qiu, Julien Niklas Siems, Russell Mendonca, Louis Schlessinger, Jeffrey Li, Georgiana Manolache, Debo Dutta, Lucas Glass, Abhishek Singh, Gregor Koehler
04:30 PM Contributed Talk 1: Meta-Learning with Warped Gradient Descent (Sebastian Flennerhag) (Talk)
04:45 PM Contributed Talk 2: MetaPix: Few-shot video retargeting (Jessica Lee) (Talk)
05:00 PM Compositional generalization in minds and machines (Talk) Brenden Lake
05:30 PM Discussion 2 (Discussion Panel)

Author Information

Roberto Calandra (Facebook AI Research)
Ignasi Clavera Gilaberte (UC Berkeley)
Frank Hutter (University of Freiburg & Bosch)

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he previously was an assistant professor 2013-2017. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.

Joaquin Vanschoren (Eindhoven University of Technology, OpenML)

Joaquin Vanschoren is an Assistant Professor in Machine Learning at the Eindhoven University of Technology. He holds a PhD from the Katholieke Universiteit Leuven, Belgium. His research focuses on meta-learning and understanding and automating machine learning. He founded and leads, a popular open science platform that facilitates the sharing and reuse of reproducible empirical machine learning data. He obtained several demo and application awards and has been invited speaker at ECDA, StatComp, IDA, AutoML@ICML, CiML@NIPS, AutoML@PRICAI, MLOSS@NIPS, and many other occasions, as well as tutorial speaker at NIPS and ECMLPKDD. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and co-organizes the AutoML and meta-learning workshop series at NIPS 2018, ICML 2016-2018, ECMLPKDD 2012-2015, and ECAI 2012-2014. He is also editor and contributor to the book 'Automatic Machine Learning: Methods, Systems, Challenges'.

Jane Wang (DeepMind)

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